Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues

By Shinichiro Wachi, Ken Yoneda, Reen Wu. 2005

412 citations recorded by the source corpus.

Motivation Global protein interaction network (interactome) analysis provides an effective way to understand the relationships between genes. Through this approach, it was demonstrated that the essential genes in yeast tend to be highly connected as well as connected to other highly connected genes. This is in contrast to the genes that are not essential, which share neither of these properties. Using a similar interactome-transcriptome approach, the topological features in the interactome of differentially expressed genes in lung squamous cancer tissues are assessed. Results This analysis reveals that the genes that are differentially elevated, as obtained from the microarray gene profiling data, in cancer are well connected, whereas the suppressed genes and randomly selected ones are less so. These results support the notion that a topological analysis of cancer genes using protein interaction data will allow the placement of the list of genes, often of the disparate nature, into the global, systematic context of the cell. The result of this type of analysis may provide the rationale for therapeutic targets in cancer treatment.

Subfields: Molecular Biology

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Citations in the corpus

EgoNet: identification of human disease ego-network modules (2014, neutral): “Similar conclusions have also been reported in the literature [61,62].”
Identification of functional modules using network topology and high-throughput data (2007, background): “Topological properties of interaction networks induced by genes active in different conditions were studied [6-9].”
Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer (2007, background): “To investigate whether the biomarker genes identified in cytologically normal large-airway epithelial cells are differentially expressed in actual lung cancer tissue, we evaluated the biomarker for its ability to distinguish between normal and cancerous lung tissue in two previously published microa”
The Roles of Post-translational Modifications in the Context of Protein Interaction Networks (2015, background): “The increased likelihood of a functional association of proteins with high interaction degree and their involvement in human disease has been reported before [52, 53].”
Network-based prediction of protein function (2007, methodology): “…works proposed a two-step approach: extraction of a group of genes that are highly expressed in a certain condition, and then analysis of the topological properties of PPI networks induced by these genes (Luscombe et al , 2004; Balazsi et al , 2005; de Lichtenberg et al , 2005; Wachi et al , 2005).”
Network Medicine: A Network-based Approach to Human Disease (2011, result): “Some studies support this hypothesis, such as the finding that the protein products of genes that are upregulated in squamous cell carcinoma of the lung tend to have a higher degree than do proteins with levels that are unaffecte”
Disease candidate gene identification and prioritization using protein interaction networks (2009, methodology): “Although PPIs have been used widely to identify novel disease candidate genes [14,15,23,26-35], besides Kohler et al.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “…analysis measures (e.g. degree, betweenness centrality, shortest path) of integrated interactome and expression data ranked cancer related proteins for target prediction, and showed their central network position (Wachi et al., 2005; Platzer et al., 2007; Chu & Chen, 2008; Mani et al., 2008).”
Network-based methods for human disease gene prediction. (2011, background): “The analysis of differentially expressed genes in cancer suggested that up-regulated genes in lung squamous cancer tissues have significantly higher connectivity in the PPI network [29].”
Protein networks in disease. (2008, methodology): “In humans, some groups report an elevated degree for disease genes ( Wachi et al. 2005; Jonsson and Bates 2006), while others do not (Goh et al. 2007).”